This invention relates generally to the field of leak detection in process systems and, more particularly, for leak detection performance in boilers such as black liquor recovery boilers of any other area where the detection of leak created mass imbalances using online measurements is of interest.
Early detection of recovery boiler leaks continues to be an important objective of power and recovery operations because of the serious consequences of a water leak into the recovery boiler furnace. The leak detection techniques currently in use can generally be classified into four categories: (1) operator observations; (2) acoustic systems; (3) chemical mass balance systems; and (4) water mass balance systems. Each method has its own inherent strengths and weaknesses. The need for multiple methods of detection as a means to overcome individual weaknesses and ensure reliable detection also has been documented.
The application of the present invention is directed to providing boiler operators with tradeoffs among sensitivity, false alarms and offline periods of leak detection systems that use water or chemical mass balance methods around a recovery boiler. For a water mass balance (WMB), flow meters around the waterside of the boiler are used to calculate the balance of water entering and leaving the boiler. The chemical mass balance (CMB) technique relies on a combination of flow measurements and chemical concentration measurements to calculate the mass balance of a specific stable and non-volatile species (such as phosphate or molybdate) around the waterside of the boiler. In either case, if a statistically significant loss is calculated a water leak is suspected and an alarm is triggered to alert the operator.
Typically, there is interest in detecting leaks of 1,000 to 10,000 lb/hr or 0.1% to 1% of a typical 500,000 lb/hr total flow. This presents a challenge when one considers the magnitude and type of noise or variation that exists in a calculated water or chemical mass balance signal. For a water mass balance system, noise arises from the inherent variability of steam and water flows, the flow meters measuring them, and the drum level control circuit. An indication of the noise associated with a calculated water mass balance is shown in Table 1. The calculated standard deviation of a water mass balance is shown for five study recovery boilers at times when their loads were relatively stable.
Three observations can be made from Table 1: First, the magnitude of the noise presents a distinct challenge in meeting the stated leak detection goal (less than 2% of steam load). Second, the magnitude of the noise varies among boilers. The differences are primarily due to the differing degrees of sophistication and care taken in tuning the drum level control circuit. The mass balance noise is primarily related to the variation in time response (lag) between an altered steam flow and the responding change in feedwater pumping rate. Third, the noise is variable for a given boiler over a daily and even weekly basis. Any water mass balance method requires some way to manage this flow-related noise.
For chemical mass balance, the situation is improved as the number of measurements and their noise levels are lower than water mass balance. One of two related approaches have been used. In the first, the concentration of a tracing or treatment chemical (entering at fixed concentration) and exiting the boiler are determined while holding the ratio of feedwater to blowdown flow fixed. In the second, the pumping rate of a chemical of known concentration is measured while the blowdown chemical concentration and flowrate are measured.
In the first case, the measurements are chemical concentrations entering and exiting the boiler. In the second, they are product chemical concentration (fixed), pumping rate of that chemical, blowdown flow and blowdown chemical concentration. Noise levels for the individual measurements of the second method have been determined and are shown in the Table 2.
In addition to the random noise discussed above, steam loads in recovery boilers often vary due to liquor heating value variation, control of liquor supply, operation of other boilers in the system, and other process influences. FIG. 1 shows the duration vs. % load drops in five recovery boilers taken over xc2xe year to 1 year time periods. The area within xc2x120% on the y-axis is assumed to be normal boiler load variations and were not plotted. As can be seen from the plots and tables, significant load changes are a regular occurrence with recovery boilers. Also, these load changes vary in duration by quite a wide range of times. Three of the five boilers studied only decrease their steam load from xe2x80x9cnormalxe2x80x9d steaming rates; two boilers both increase and decrease load. Steam load changes affect water mass balance leak detection systems in one of two ways: (1) Load swings alter the steam to water ratio in the boiler and thus the total mass. With a lower steam to water ratio expected at lower load, the boiler water mass increases. As the load is decreased, the mass increases which may lead to a false alarm; (2) Flow meter calibration errors vary with steam load. Demonstration of the combined effect is shown in FIG. 2 where a load drop from 500 klb/hr to 350 klb/hr leads to an apparent 15 klb/hr xe2x80x9cleakxe2x80x9d in a raw water mass balance.
Load changes also affect chemical mass balance systems. As the load decreases, the amount of water present in the boiler increases which dilutes the tracer or treatment chemical potentially leading to a false alarm. When the load increases back to normal, the mass of water decreases making the tracer concentration increase. The characteristic of this type of change is a sharp change in chemical concentration as the load is changed.
As can be seen from these curves plotted in FIG. 3, there is a strongly likelihood that such load drops can lead to false alarms. Given the number and duration of these load changes, mass balance systems not correcting for these will spend significant time in a false alarm state. Using the data from the five boilers shown in FIG. 1, estimates were made as shown in Table 3.
Based on the data from these five boilers, a mass balance not correcting for load changes could expect false alarms due solely to load changes on average every seven to fourteen days with times in alarm condition between 2% and 9%. Mass balance systems which shut down when load changes occur would be offline at these times. Alternatively, if a system were designed to avoid false alarms, but was not designed to provide load swing correction or disabling, detection limits would be relaxed to the point where the system would not be a useful detection tool.
There are other system changes that can affect mass balance measurements. One with a potentially large impact are boiler startups especially those where the boiler has been down for more than a day.
Mass balances (chemical and water) are unstable during startups. The flows will be outside normal operation and the boiler water will change as cold water is converted to a mixture of steam and water with increased steam load. To better understand this phenomena, an extensive analysis of ten boiler startups was completed for one boiler system. FIG. 4 shows steam flow and a smoothed raw water mass balance for a typical boiler startup.
The overall mass balance does not stabilize for fifteen to twenty hours. A similar situation is observed for chemical mass balance systems. An effective mass balance-based leak detection system must be able to avoid the false alarms associated with mass balance instabilities.
There are other situations where the mass balance (especially water mass balance) is briefly upset. Some of these include over-pressurization venting, momentary drum level upsets, and manual blowdown. Additionally, some boiler processes have periodic oscillations such as drum level variation (fast) or flow meter drift (slow). An effective system must deal with these without generating unnecessary false alarms.
To detect leaks using a water mass balance, all the flows of water into and out of the boiler are measured. FIG. 5 depicts an exemplary water mass balance level detection system 1. In particular, the system 1 comprises a recovery boiler 2 having a feedwater flow 3, a steam flow 4 and a blowdown flow 5. A feedwater flow signal 6, blowdown flow signal 7 and steam flow/drum level signal 8 are all conveyed to an input/output device 9. This in turn feeds these signals to a computer workstation 10 which comprises the leak detection software. For example, the system and method of application Ser. No. 08/938,191 uses these flow measurements to calculate the boiler water mass balance. If the boiler water mass balance (mass inxe2x88x92mass out) increases significantly a leak is suspected. Hardware requirements for water mass balance system are relatively simple. Temperature and pressure compensated flow signal must be available to close the water mass balance. In some cases additional flow signals such as attemperation water flow or sootblower steam flow may be needed if required to close the water mass balance.
Hardware requirements for chemical mass balance systems are more extensive than for water (see FIG. 6). FIG. 6 depicts an exemplary chemical mass balance leak detection system 11. The amount of chemical feed into the boiler 2 via a chemical feedline 12 is determined using a verified chemical feed 13 and control system, the latter of which comprises a chemical tank 14, a pump 15, and a controller 18 (e.g., the BetzDearborn Pacesetter Plus Controller); also a sample line and sample system 16 and residual analyzer 17 are used for determining chemical concentration. The amount leaving the boiler is determined by measuring blowdown flow rate and the chemical concentration. If a discrepancy in chemical mass balance is detected, a leak is suspected. The sample system has been designed that incorporates a special high pressure filter to allow for the continuous reliable measurement of a blowdown sample.
Having reliable equipment is a necessary but insufficient prerequisite for an effective leak detection system. As described above, there are many factors influencing chemical and water mass balance measurements in recovery boilers causing variation even when no leaks are present. Thus, the goals in leak detection are to detect as small a leak as possible, as quickly as possible, without false alarms and minimal down time.
Optimal reduction in noise related to flow and flow meters is achieved by using averaging techniques such as those disclosed in application Ser. No. 08/938,191, which include:
exponential-weighting is used to provide moving averages of a wide range of times (one minute averages for up to a 16 hour period) without consuming huge amounts of computer memory;
the problem of over-averaging leading to slow response for fast growing leaks, or under-averaging leading to loss of sensitivity for slow growing leaks, is handled by a having a series of averaging windows ranging from 30 minutes up to 16 hrs. These are combined to form one overall leak detection statistic that chooses the window with the most significant statistic at a particular time; and
background subtraction using a moving average of much longer window than the expected leak growth rates is used to remove the effect of long-term (days to weeks) drift in flow meter output.
As noted above, even with optimal flow-related noise reduction, the problem of steam load-related noise can be acute in some systems leading to false alarms on a weekly basis. To correct for the artifacts introduced with load changes, load compensation algorithms have been developed such as those disclosed in U.S. Pat. No. 5,817,927 (Chen et al), which is assigned to the same Assignee as the present invention and whose entire disclosure is incorporated by reference herein.
There are two parts to these corrections for both chemical mass balance and water mass balance methods. FIGS. 7A-7C show a boiler load swing demonstrating the effectiveness of a two-step approach to largely eliminate the effect on water mass balances. FIG. 7A shows the raw water mass balance data and the steam flow. The first correction (FIG. 7B) handles the load-related offsets discussed above which provides a correction for the steam and feedwater flow calibrations. As shown in FIG. 7B, the resulting data is much closer to the unperturbed baseline needed for reliable leak detection. However there still are disturbances at the beginning and end of the load swing. These are corrected by a second term which accounts for the differences in time response between the feedwater and steam flow signals. FIG. 7C depicts both of these corrections incorporated therein.
Similar corrections can be applied to the chemical mass balance method. The results are shown in FIG. 8A (using a first chemical mass balance correction term) and FIG. 8B (using the first correction term as well as a second chemical mass balance correction term).
The startup of a cold boiler presents a difficult challenge to mass balance methods as there is no reliable way to know how the boiler load will be raised or how the boiler will respond. There can be other events that disrupt the mass balance. Some of these mentioned above include venting, drum level upsets, and manual blowdowns. For both startups and other events where the chance of a false alarm is very high, one option for increasing the reliability of a leak detection system is to bring the detection system down until the boiler condition is returned to normal.
In light of all of the above, the need to predict individual leak detection system performance prior to actual leaks has been overlooked. All of the above corrections are aimed at addressing background and system noise for a particular boiler. As demonstrated above, the noise and leak detection sensitivity are boiler specific. Thus, the ability to predict leak detection system performance presents some challenges.
Thus, there remains a need for a method for predicting the performance of any recovery boiler leak detection system that uses mass balancing by presenting the operator of the recovery boiler with tradeoffs regarding the sensitivity of the leak detection system, the number of false alarms of that system as well as the amount of system downtime.
Accordingly, it is the general object of the instant invention to provide an apparatus and methods for meeting that need.
It is a further object of this invention to provide an method for presenting tradeoffs among the sensitivity, false alarms and off-line time of a recovery boiler leak detection system.
It is still yet another object of the present invention to provide a method for presenting tradeoffs among the sensitivity, false alarms and off-line time of a recovery boiler leak detection system whereby the sensitivity is expressed as a rate for a given window of time, e.g., 7500 lbs/hour in 1 hour.
It is still yet a further object of this invention to provide a method for presenting tradeoffs among the sensitivity, false alarms and off-line time of a recovery boiler leak detection system based on water mass balance.
It is yet another object of this invention to provide a method for presenting tradeoffs among the sensitivity, false alarms and off-line time of a recovery boiler leak detection system based on chemical mass balance.
It is yet another object of this invention to provide a method for presenting tradeoffs among the sensitivity, false alarms and off-line time of a recovery boiler leak detection system based on a fixed concentration of chemical into and out of the recovery boiler.
It is still yet another object of the present invention to provide a method for characterizing the performance of a leak detection system for a recovery boiler.
It is still yet even another object of the present invention to provide a method for characterizing the performance of a leak detection system for a recovery boiler based on the particular operation of the recovery boiler.
It is still yet a further object of this invention to provide a method for presenting tradeoffs among the sensitivity, false alarms and off-line time of a recovery boiler leak detection system used with a recovery boiler that may or may not be base-loaded.
These and other objects of the present invention are achieved by providing a method for presenting tradeoffs of the sensitivity, false alarms and offline operation of a recovery boiler leak detection system. The method comprises the steps of: (a)obtaining leak-free operational data from the recovery boiler; (b) specifying a leak probability estimating filter (e.g., a filter having a mass balance-based leak flow estimation model of the recovery boiler, a statistical noise model and a model of how typical leaks grow over time); c) generating a numerical indicator (e.g., a leak probability statistic) from the filter and the operational data and wherein the numerical indicator has an output that is a measure of leak likelihood; (d) specifying a condition or conditions wherein the numerical indicator output is undefined; (e) selecting an alarm limit for the recovery boiler leak detection system wherein if said numerical indicator output exceeds the limit, an alarm is activated in the recovery boiler leak detection system; (f) determining the sensitivity of the leak detection system from one of a first sequence of numerical indicator outputs that exceeds the alarm limit in the least amount of time and wherein the first sequence of numerical indicator outputs is generated from simulated recovery boiler inputs and an assumed leak that are fed into the filter; (g) determining the number of false alarms and offline times from a second sequence of numerical indicator outputs that exceed the alarm limit or are undefined, respectively, and wherein the second sequence of numerical indicator outputs is generated by a sequence of the operational leak-free data that are fed into the filter; and (h) presenting tradeoffs among the sensitivity, false alarms and offline times.